A concept design for an ultra-long-range survey class AUV

Gliders and flight-style Autonomous Underwater Vehicles (AUVs) are used to perform perform autonomous surveys of large areas of open ocean. Glider missions are characterized by their profiling flight pattern, slow speed, long range (1000s of km) and many month mission duration. Flight-style AUV missions are faster, of shorter range (100s of km) and multi day duration. An AUV combining many aspects of both vehicle classes would be of considerable value.This paper investigates the factors that affect the range of a traditional flight-style AUVs. A generic range model is outlined which factors in the effects of buoyancy on the range. The model shows that to create a very long range AUV it is necessary to reduce the hotel load on the AUV to the order of 1W and to add wings to overcome the vehicle’s positive buoyancy whilst travelling at the reduced speed required for long range.Using this model a concept long range AUV is outlined that is capable of travelling up to 5000km. The practical issues associated with achieving this range are also discussed

Advancing Climate Change Research and Hydrocarbon Leak Detection : by Combining Dissolved Carbon Dioxide and Methane Measurements with ADCP Data

With the emergence of largescale, comprehensive environmental monitoring projects, there is an increased need to combine state-of-the art technologies to address complicated problems such as ocean acidifi cation and hydrocarbon leak detection

Navigation Control of an Automated Guided Underwater Robot using Neural Network Technique

In recent years, under water robots play an important role in various under water operations. There is an increase in research in this area because of the application of autonomous underwater robots in several issues like exploring under water environment and resource, doing scientific and military tasks under water. We need good maneuvering capabilities and a well precision for moving in a specified track in these applications. However, control of these under water bots become very difficult due to the highly non-linear and dynamic characteristics of the underwater world. The logical answer to this problem is the application of non-linear controllers. As neural networks (NNs) are characterized by flexibility and an aptitude for dealing with non-linear problems, they are envisaged to be beneficial when used on underwater robots. In this research our artificial intelligence system is based on neural network model for navigation of an Automated Underwater robot in unpredictable and imprecise environment. Thus the back propagation algorithm has been used for the steering analysis of the underwater robot when it is encountered by a left, right and front as well as top obstacle. After training the neural network the neural network pattern was used in the controller of the underwater robot. The simulation of underwater robot under various obstacle conditions are shown using MATLAB